The main objective of this study is to propose an optimal transport based semi-supervised approach to learn from scarce labelled image data using deep convolutional networks. The principle lies in implicit graph-based transductive semi-supervised learning where the similarity metric between image samples is the Wasserstein distance. This metric is used in the label propagation mechanism during learning. We apply and demonstrate the effectiveness of the method on a GNSS real life application. More specifically, we address the problem of multi-path interference detection. Experiments are conducted under various signal conditions. The results show that for specific choices of hyperparameters controlling the amount of semi-supervision and the level of sensitivity to the metric, the classification accuracy can be significantly improved over the fully supervised training method.

翻译：本研究的主要目标是提出一种基于最优传输的半监督方法，利用深度卷积网络从稀缺的标注图像数据中学习。其原理基于隐式图归纳半监督学习，其中图像样本间的相似性度量采用Wasserstein距离。该度量在学习过程中被用于标签传播机制。我们将该方法应用于GNSS实际场景，并验证其有效性。具体而言，我们解决了多径干扰检测问题。实验在不同信号条件下进行。结果表明，通过合理选择控制半监督程度和度量敏感度的超参数，分类准确率相较于全监督训练方法可获得显著提升。